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Classifier: keep feature values at the training minimum in the first bin #1267

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Classifier: keep feature values at the training minimum in the first bin #1267

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4 changes: 4 additions & 0 deletions suite2p/classification/classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,7 +133,11 @@ def _get_logp(self, stats):
x[x < self.grid[0, n]] = self.grid[0, n]
x[x > self.grid[-1, n]] = self.grid[-1, n]
x[np.isnan(x)] = self.grid[0, n]
# np.digitize(..., right=True) returns 0 for x == grid[0], which after
# the -1 would index the *last* bin; clip so values at (or clamped to)
# the minimum use the first bin and values at the maximum the last.
ibin = np.digitize(x, self.grid[:, n], right=True) - 1
ibin = np.clip(ibin, 0, self.p.shape[0] - 1)
logp[:, n] = np.log(self.p[ibin, n] + 1e-6) - np.log(1 - self.p[ibin, n] +
1e-6)
return logp
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